Systems and methods for discovering social accounts

ABSTRACT

Methods and systems allow organizations to discover accounts, subscriptions, properties, sites and other online portals within each distinct social network platform and across disparate social network platforms, publishing platforms and networks that represent, claim to represent or are relevant to their organization and/or brands based on search terms and facilitate the statistical reporting and analysis of activities on the discovered properties.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. § 120 of the filing date of U.S. patent application Ser.No. 15/680,973, filed Aug. 18, 2017, entitled “SYSTEMS AND METHODS FORDISCOVERING SOCIAL ACCOUNTS,” which is a continuation of, and claims abenefit of priority under 35 U.S.C. § 120 of the filing date of U.S.patent application Ser. No. 13/864,815, filed Apr. 17, 2013, now U.S.Pat. No. 9,747,372, entitled “SYSTEMS AND METHODS FOR DISCOVERING SOCIALACCOUNTS,” which claims a benefit of priority from U.S. ProvisionalApplication No. 61/625,351, filed Apr. 17, 2012, entitled “SYSTEMS ANDMETHODS FOR IDENTIFYING, CATEGORIZING AND CLASSIFYING SOCIAL ACCOUNTSFOR AN ORGANIZATION ACROSS DISPARATE SOCIAL NETWORKING PLATFORMS,” theentire contents of which are hereby expressly incorporated by referencefor all purposes.

FIELD OF THE INVENTION

The invention relates generally to social networking and advertising,and, more specifically, to techniques and supporting systems foridentifying, categorizing, and classifying media properties within and,across and among distinct social network platforms.

BACKGROUND

Social networking platforms and networks including FACEBOOK, GOOGLE+,LINKEDIN, TWITTER, YOUTUBE, XING, and many others are well establishedand have millions of subscribers. In fact, these networks have become sopervasive that they are commonly used by organizations as an advertisingplatform as well as a conduit for communicating with their customers,clients, alumni, and target audiences. Creating accounts on thesedisparate networks is relatively simple and allows individual users toeasily create properties on behalf of an organization, whethersanctioned by the organization or not. In many cases, these disparatenetworks do not have any mechanisms to accurately define or verify theactual relationship of one of these accounts to the respectiveorganization it may be representing. Also, these platforms have nocentral way to aggregate, consolidate or track multiples of theseproperties, rate their relevance and relationship to an organization. Asa result the social networks cannot accurately describe theirrelationship with other properties or accounts on other platforms andnetworks relative to a single organization. Furthermore, there is no waylogically organize these accounts based on their relevance andrelationship to that respective organization.

The rapid growth and lower barrier of entry for creating these accountsand properties, combined with the inability to easily identify, organizeand track accounts related to an organization is a growing challenge fororganizations. Given that these properties are created to represent thebrand of the organization and communicate with their customers,prospective customers, partners, and influencer audiences, the lack ofawareness, visibility, and organization of these properties representsmissed opportunities and potential risks for the organization.

Therefore, there is a need for systems and techniques to alloworganizations to automatically and programmatically discover propertiesacross social network platforms and networks that are related to theirrespective organization independent of any one platform or network.Moreover, these organizations desire the ability to rate the relevanceof the discovered accounts and properties, categorize them based ontheir affiliation and relevance, and track and measure their use,prevalence and effectiveness based on these characteristics.

SUMMARY OF THE INVENTION

The invention provides various techniques and supporting systems thatallow organizations to discover accounts, subscriptions, properties,sites and other online portals (referred to collectively herein as“social properties”) within each distinct social network platform andacross disparate social network platforms, publishing platforms andnetworks that represent, claim to represent or are relevant to theirorganization and/or brand(s) based on a particular search term (orterms).

As a result, the invention returns sets of properties relevant to theorganization based on user-specified terms and relationships among theterms. The results may then categorized, grouped and reported based on aset of classifications resulting from relevance scores that attributethe properties into categories related to the organization. Thiscategorization can include, as an example, differentiating among companyaccounts and individual accounts, where the individual(s) have arelationship to the company but who may or may not speak on behalf ofthe company. The identified properties are determined based, forexample, on a scored and matched set of attributes of each propertycultivated and correlated from multiple sources. This includes theintegration and combination of searches and matches of terms fromrespective social network platform application programming interfaces(APIs), custom Internet search engine queries, content classificationengines, custom algorithms, and Boolean operators.

Therefore, in one aspect, the invention provides a method, implementedon a computer, for correlating multiple social network properties acrossmultiple disparate social network platforms wherein each property isassociated with an entity. The computer includes a memory for storingcomputer executable instructions and a processing unit for executing theinstructions. When executed, the instructions facilitate the submissionof search strings to the disparate social network platforms via aplurality of APIs, wherein each API is associated with a respectivesocial network platform and the search strings are related to theentity. The instructions further facilitate the receipt, of uniqueproperty identification data for social network properties existing onthe social network platforms that match the search strings. The receiveddata is normalized such that the unique property identification data maybe stored in a database as associated with the single entity and storedin the normalized unique property identification data in physicalmemory.

In some embodiments, the search strings include multiple terms, and, incertain instances, a weighting or correlation among the terms may beprovided. The weighting can represent, for example, a relationship amongthe terms, and the correlation may be positive or negative. In someinstances, the submission of search strings may be devoid of user logincredentials, whereas in other cases the search strings may be submittedwith user credentials.

At least a subset of the APIs are provided by the respective socialnetwork platforms, and certain other APIs may be provided by thirdparties not related to the social network platforms.

The unique property identification data received in response tosubmission of the queries may include, for example, a property name,user generated content created by users associated with the respectivesocial network property, user generated content created by usersunaffiliated with the respective social network property, securitysettings associated with the respective social network property, and/orview statistics associated with the respective social network property.In certain instances, the unique property information data comprisesindications of the entities' interactions with the properties associatedwith the respective entities' social network properties and updating theone or more search strings based thereon. In cases in which the searchstrings include at least two search strings, the received may becategorized based on a relationship of those properties to theorganization. A graphical user interface may also be provided forpresenting the unique property information data and facilitating userinteraction therewith.

In another aspect, the invention provides a system for correlatingmultiple social network properties across multiple disparate socialnetwork platforms, wherein each property is associated with a singleentity. The system includes a memory for storing computer executableinstructions and a processing unit for executing the instructions storedin the memory. Execution of the instructions results in theinstantiation of a query module and a data normalization module. Thequery module, when executed, facilitates the submission of searchstrings to the multiple disparate social network platforms via APIs,wherein each API is associated with at least one of the respectivesocial network platforms and each of the search strings is related tothe single entity. The query module also receives unique propertyidentification data for social network properties existing on the socialnetwork platforms that match the search strings. The data normalizationmodule, when executed, normalizes the unique property identificationdata such that the unique property identification data may be stored ina database as associated with the single entity and stores thenormalized unique property identification data in physical memory.

In some embodiments, the search strings include multiple terms, and, incertain instances, a weighting or correlation among the terms may beprovided. The weighting can represent, for example, a relationship amongthe terms, and the correlation may be positive or negative. In someinstances, the submission of search strings may be devoid of user logincredentials, whereas in other cases the search strings may be submittedwith user credentials.

At least a subset of the APIs are provided by the respective socialnetwork platforms, and certain other APIs may be provided by thirdparties not related to the social network platforms.

The unique property identification data received in response tosubmission of the queries may include, for example, a property name,user generated content created by users associated with the respectivesocial network property, user generated content created by usersunaffiliated with the respective social network property, securitysettings associated with the respective social network property, and/orview statistics associated with the respective social network property.In certain instances, the unique property information data comprisesindications of the entities' interactions with the properties associatedwith the respective entities' social network properties and updating theone or more search strings based thereon. In cases in which the searchstrings include at least two search strings, the received may becategorized based on a relationship of those properties to theorganization. A graphical user interface may also be provided forpresenting the unique property information data and facilitating userinteraction therewith.

BRIEF DESCRIPTION OF THE FIGURES

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the invention.

FIG. 1 is a block diagram of a system for discovering and categorizingsocial network properties across multiple social network platformsaccording to an embodiment of the invention.

FIG. 2 is an exemplary user interface for entering search query termsaccording to an embodiment of the invention.

FIG. 3 is an exemplary user interface for viewing a summary of searchquery results according to an embodiment of the invention.

FIG. 4 is an exemplary user interface for filtering search query resultsaccording to an embodiment of the invention.

FIG. 5 is an exemplary user interface for saving search query resultsinto a database according to an embodiment of the invention.

FIG. 6 is an exemplary user interface for categorizing search queryresults according to an embodiment of the invention.

FIG. 7 is a flow chart depicting steps performed in categorizing searchquery results according to an embodiment of the invention.

FIG. 8 is an exemplary user interface illustrating the categorization ofsocial network properties based on an affiliation with an entity andindividuals associated with the entity.

FIG. 9 is an exemplary user interface for graphically depictingstatistical characteristics of search query results according to anembodiment of the invention.

FIG. 10 is an exemplary user interface for depicting statisticalcharacteristics of search query results according to an embodiment ofthe invention.

FIG. 11 is an exemplary user interface for depicting the categorizationof social network properties within a single platform.

DETAILED DESCRIPTION

FIG. 1 illustrates, generally, a process for discovering social networkproperties related to an organization. Generally, social networkproperties refers to accounts, profiles, pages, or other terms used todescribe user-specific or organization-specific properties and accountsset up within an established social network platform such as FACEBOOK,TWITTER, GOOGLE+, BING, INSTAGRAM, AND LINKEDIN as well as websites andpages set up using site hosting services such as GoDaddy, Web.com, etc.The term may also apply to “custom” properties that are designed andhosted by the organization itself, such as its corporate and/or consumerwebsite.

Initially, a member or representative of the organization provides a setof search strings, configuration parameters, and in some cases usercredentials and authorizations 105 that are used as input into theprocess. The search strings may, in some cases, be a single word or term(e.g., NIKE or ROLLERBLADE). In other instances, the search strings mayinclude more than one term and may include a probabilistic weightingthat indicates the strength of the relationship between the two terms.For example, DELTA AIRLINES may use the terms Delta and Airlines with a95% weighting, to avoid receiving results related to a Delta faucetcompany and/or a Delta dental insurance company.

Nike may use the terms NIKE and RUNNING, but attribute a lower weightingto the terms. Although described herein as a percentage, the weightingmay be described using any quantitative terms (e.g., 1-10) or evenqualitative terms (e.g., weak, neutral, strong) that are then convertedinto quantitative terms for processing. In some instances, there may bea collection of terms used such as a group of brands that, other thanbeing owned and/or distributed by the same entity, have little or norelationship. For example, PROCTOR AND GAMBLE may use [(CREST andTOOTHPASTE) and (GILETTE and RAZOR)] as a single search string, knowingthat the results will include properties that will eventually be viewedand analyzed separately.

In some cases, access to the properties and/or the data and contentassociated with the properties may be restricted and requireaccount-specific user credentials. In such cases, users may include thecredentials with each search string. In some cases, multiple sets ofcredentials may be provided if, for example, the user is aware ofmultiple properties that require different credentials.

Once identified, the search strings are submitted to a query processingmodule 110. The query processing module 110 accepts the search stringsand formats the strings and terms as required by individual platformAPIs 115. The APIs are typically provided by the platforms themselves,but in some cases custom APIs may be developed to access data held bythe platforms. For example, an API may require search strings to bepresented in a particular format, coupled with the any user credentialsassociated with one or more properties. In other cases, the APIs mayrequire an indication as to whether the search is to be performed onpublicly available data only, or that it is to include data limited toowners of the properties that present the correct credentials.

Regardless of the form or source of the APIs, because they are typicallydesigned and developed independently of each other, the results receivedfrom the APIs at the query module are likely in different formats and,in their raw form, are not easily viewed and reported. As such, theresults may be provided to a normalization module 120 that identifiesmetadata that is common to each record and formats the data accordingly.For example, the fields containing the property name may be differentamong the various platforms (e.g., USERID, MEMBER_ID, ACCT_ID, etc.).The normalization module 120 uses known and, in some cases, discoveredrelationships among the received records to allow for consistent datastorage, aggregation and reporting. In some cases, additional data maybe added to the received data that provides additional categorization orclassification functionality 125, as described in greater detail below.

Once the received data is normalized and in final form, it is stored ina physical data store 130 for subsequent reporting and analysis. Thedata store 130 may be user-specific and implemented as a uniqueinstantiation of a commercially-available database management systemsuch as ORACLE, MySQL, or others, or on a cloud-based data storageservice such as provided by AMAZON's RDS. Once stored thereon, the datamay be presented and reported using a display device 140, such as acomputer terminal, mobile device, tablet, or othercommercially-available displays. Examples of the specific interfaces andreports are described in greater detail below.

In some embodiments, a scheduler module 145 is provided thatautomatically executes predefined and stored query strings on a periodic(e.g., weekly, monthly, etc.) basis.

FIG. 2 illustrates an exemplary user interface for using the systems andmethods described herein. A user may select between either a “Company”based search or a “Custom” search using, in this example, a set of radiobuttons 205. If “Company” is selected, the user may enter the name ofthe company in a dialog box 210, and a conventional web-based search isexecuted using the company name. If, however, the user selects “Custom”the user is presented with a listing 215 of various social networkplatforms to search. In addition, the user is provided a text box 220 inwhich she may provide the custom search string that is to be submittedto the selected platforms using their respective APIs. In someinstances, searches may be saved and reused such that complex searchstrings need not be reentered, and may be edited to reflect new searchterms and results of prior searches.

The received results may be delivered as a raw result, exported to alist, and/or displayed to the user. However, additional filtering andcategorization features allow users to dig deeper into the results andidentify trends and key properties and use the results to betterallocate resources, capital and marketing efforts. FIG. 3 illustrates anexemplary user interface that allows users to filter and scan theresults. For example, the results can be further refined by applyingfilters that limit the properties based on activity dates 305, activitylevels 310, and in some cases inclusion and exclusion terms, and contenton the properties themselves for topical relevance to the organization.This allows users to eliminate so-called “dead” properties that have hadno activity or views within some time period, and/or filter outproperties with little or no “engagement”—meaning pages or accounts withminimal views or impressions over some period of time. Once thesefilters are applied, the results are redisplayed as a general listing315 that includes the number of properties within each of the selectedplatforms. In some embodiments, the line item indicating the number ofproperties returned for each platform may be implemented as anexpandable list such that when a user selects the platform, a listing ofthe individual properties is displayed. The display may be ordered byrecency (e.g., date of creation, date of last update, date of last post,etc.), relevancy (degree of match to the search string), or engagement(e.g., number of posts, likes, links, etc.).

Referring now to FIG. 4, the user may select a subset of the receivedproperties (either by date range, keyword from the search string, term,platform, or any combination thereof) and drill down into theproperties. For example, for a search string of [FORD and MUSTANG] theremay be hundreds of Facebook accounts, ranging from officialFord-sponsored and managed pages to pages run by car enthusiasts,mechanics, etc. For any given subset, the system allows for a secondaryquery 405 that scans and returns all the publicly available contentassociated with the property. For example, if a property is a TWITTERaccount, all the TWEETS associated with that account may be returned. Insome examples, the results may be further filtered based on dates, newcontent since the last query, and other characteristics.

Once the results have been returned from the query module, filtered and,in some cases, annotated with classification metadata, the results canbe named and saved as a distinct discovery search. The named discoveriescan be revisited to further refine or organize its results.Additionally, discovery searches may be set to run automatically with acertain periodicity (e.g., every week) and create notifications (email,for example) if new properties have been discovered or of new propertiesmeeting certain stored criteria.

Referring to FIG. 5, the received properties may be further organizedinto specific categories of brand relevance and affiliation. Thesediscrete categories of properties can then be tracked individually orgroups of categories can be tracked in aggregate. The categories can bedefined and applied automatically, defined automatically and applied bythe user, or created and applied as custom categories by the user. Ininstances in which the classification module automatically organizes andclassifies the properties, the key attributes and metadata associatedwith the properties are used to categorize the properties. In addition,the search string 505 used to generate the list of properties may beused to categorize the properties. In some instances, the categorizationmay relate to the structure of the social networks (e.g., pages,accounts, profiles, groups, etc.) 510.

Alternatively, the user may create custom, named categories 510 andassign the property or properties to the custom category or categories.Once the category or categories are assigned the properties may betracked and viewed as a category or groups of categories. These viewsfacilitate analysis of various key attributes of the properties andcategories as a onetime snapshot and over a period of time. The storedviews include views for social network platform distribution ofproperties by category, comparison of size and distribution ofcategories in terms of number of properties, risk and relevance ofcategories based on the content of properties in the category, andactivity or engagement of properties in a category or categories. All ofthese views are unique derivatives of the discovery and categorizationof properties and can be viewed and saved for subsequent reporting anddisplay.

Referring to FIG. 6, stored search results may be combined with othersearch results or groupings of results based on categories, sources(specific platforms), and/or classifications. For example, a search forproperties related to [FORD and MUSTANG] may be annotated with searchresults from a stored list 605 of other searches. Moreover, propertiesreturned in certain stored results sets may also be filtered (i.e.,excluded from) the results of the current search.

FIG. 7 illustrates a general data flow of the classification process.Generally, the content and metadata associated with the discoveredproperties 705 are received and stored in the system for reporting,filtering, aggregation and analysis. Users can manually classify theresults 710 using any term(s) or categories based on custom reportingneeds. For example, using the [FORD and MUSTANG] example from above, auser may be interested in images posted on Facebook of Mustangs thatdate from the late 1960's for an upcoming car rally, to gauge currentpricing trends, or to understand the demand for replacement parts. Sucha filter or classification may only be needed on an infrequent basis andbe so specialized that the automatic categorization may not support sucha query. Furthermore, the classification engine may apply automatedtechniques 715 using, for example, machine learning, natural languageprocessing, text analysis, as well as others to classify the results.For example, the algorithms and Boolean operators use frequency of termappearance in the property's metadata, location of the term in metadataor displayed fields, combinations of the term with other key indicatorslike the social network platform's classification for the property (ifavailable), the appearance of the property and terms in general internetsearches, and the combination of other categories of content in/on theproperty. The automated and manual classification methods may also beused in concert with each other to produce a categorization ofproperties 720 based on user-defined terms and structural metadataassociated with the properties.

FIG. 8 illustrates one example of the categorization of properties(TWITTER feeds) related to an entity (DELL). In this instance, thesystem discovered numerous feeds. A subset of these feeds is deemed tobe “official” DELL feeds that are sanctioned operated by DELL 805. Otherfeeds 810 may be loosely associated with DELL based on the individualresponsible for the feed (an employee, financial analyst, third partyservice organization, reseller, etc.) but not “official” DELL feeds.This differentiation allows marketing and sales staffs within the entityto determine the actual reach of their official properties and monitorthird-party sites for interesting, incorrect, offensive, or otherwiseimproper content, as well as promoting those properties that providevaluable information or services.

Once the resulting list of properties has been classified andcategorized in terms of each property's relationship to the entityexecuting the searches, the attributes of each category of propertiesrelated to the entity as well a comparison of individual or groups ofcategories to each other may be displayed. Referring to FIGS. 9-11, thisincludes creating a normalized view of activity across a property orgroup of properties on a social networking platform or across a group ofdisparate social networking platforms such that statistical aggregationsand analysis can be run against the received results. For example, andreferring to FIG. 8, properties may be viewed graphically within anindividual social network platform and across disparate social networkplatforms and by categories to understand the reach of the entitiesproperties 905, the distribution across networks 910, and as a compositeof reach by network 915. FIG. 10 illustrates similar data viewed intabular fashion.

FIG. 11 illustrates the categorization of properties received from asingle platform (TWITTER), illustrating the number of properties thatare deemed to be “company” accounts (feeds) and those that are deemed tobe individual accounts (feeds).

The systems and methods described herein may be implemented on andpracticed using any communications network capable of transmittingInternet protocols. A communications network generally connects a clientwith a server, and in the case of peer to peer communications, connectstwo peers. The communication may take place via any media such asstandard telephone lines, LAN or WAN links (e.g., T1, T3, 56 kb, X.25),broadband connections (ISDN, Frame Relay, ATM), wireless links (802.11,Bluetooth, 3G, CDMA, etc.), and so on. The communications network maytake any form, including but not limited to LAN, WAN, wireless (WiFi,WiMAX), near-field (RFID, Bluetooth). The communications network may useany underlying protocols that can transmit Internet protocols, includingbut not limited to Ethernet, ATM, VPNs (PPPoE, L2TP, etc.), andencryption (SSL, IPSec, etc.)

The methods may be practiced with any computer system configuration,including hand-held wireless devices such as mobile phones or personaldigital assistants (PDAs), multiprocessor systems, microprocessor-basedor programmable consumer electronics, minicomputers, mainframecomputers, computers running under virtualization, etc.

The methods and systems may also be implemented and practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules may be located inboth local and remote computer storage media including memory storagedevices.

The data store may be embodied using any computer data store, includingbut not limited to, relational databases, non-relational databases(NoSQL, etc.), flat files, in memory databases, and/or key value stores.Examples of such data stores include the MySQL Database Server or ORACLEDatabase Server offered by ORACLE Corp. of Redwood Shores, Calif., thePostgreSQL Database Server by the PostgreSQL Global Development Group ofBerkeley, Calif., the DB2 Database Server offered by IBM, Mongo DB,Cassandra, or Redis.

The system may be implemented on any computer system, which may includea general purpose computing device in the form of a computer including aprocessing unit, a system memory, and a system bus that couples varioussystem components including the system memory to the processing unit.

Computers typically include a variety of computer readable media thatcan form part of the system memory and be read by the processing unit.By way of example, and not limitation, computer readable media maycomprise computer storage media and communication media. The systemmemory may include computer storage media in the form of volatile and/ornonvolatile memory such as read only memory (ROM) and random accessmemory (RAM). A basic input/output system (BIOS), containing the basicroutines that help to transfer information between components, such asduring start-up, is typically stored in ROM. RAM typically contains dataand/or program modules that are immediately accessible to and/orpresently being operated on by processing unit. The data or programmodules may include an operating system, application programs, otherprogram modules, and program data. The operating system may be orinclude a variety of operating systems such as Microsoft Windows®operating system, the Unix operating system, the Linux operating system,the Mac OS operating system, Google Android operating system, Apple iOSoperating system, or another operating system or platform.

At a minimum, the memory includes at least one set of instructions thatis either permanently or temporarily stored. The processor executes theinstructions that are stored in order to process data. The set ofinstructions may include various instructions that perform a particulartask or tasks. Such a set of instructions for performing a particulartask may be characterized as a program, software program, software,engine, module, component, mechanism, or tool.

The system may include a plurality of software processing modules storedin a memory as described above and executed on a processor in the mannerdescribed herein. The program modules may be in the form of any suitableprogramming language, which is converted to machine language or objectcode to allow the processor or processors to read the instructions. Thatis, written lines of programming code or source code, in a particularprogramming language, may be converted to machine language using acompiler, assembler, or interpreter. The machine language may be binarycoded machine instructions specific to a particular computer.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, Basic, C, C++, CSS, HTML,Java, SQL, Perl, Python, Ruby and/or JavaScript, for example. Further,it is not necessary that a single type of instruction or programminglanguage be utilized in conjunction with the operation of the system andmethod of the invention. Rather, any number of different programminglanguages may be utilized as is necessary or desirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module.

The computing environment may also include otherremovable/non-removable, volatile/nonvolatile computer storage media.For example, a hard disk drive may read or write to non-removable,nonvolatile magnetic media. A magnetic disk drive may read from orwrites to a removable, nonvolatile magnetic disk, and an optical diskdrive may read from or write to a removable, nonvolatile optical disksuch as a CD-ROM or other optical media. Other removable/non-removable,volatile/nonvolatile computer storage media that can be used in theexemplary operating environment include, but are not limited to,magnetic tape cassettes, flash memory cards, digital versatile disks,digital video tape, solid state RAM, solid state ROM, Storage AreaNetworking devices, solid state drives, and the like. The storage mediaare typically connected to the system bus through a removable ornon-removable memory interface.

The processing unit that executes commands and instructions may be ageneral purpose computer, but may utilize any of a wide variety of othertechnologies including a special purpose computer, a microcomputer,mini-computer, mainframe computer, programmed microprocessor,micro-controller, peripheral integrated circuit element, a CSIC(Customer Specific Integrated Circuit), ASIC (Application SpecificIntegrated Circuit), a logic circuit, a digital signal processor, aprogrammable logic device such as an FPGA (Field Programmable GateArray), PLD (Programmable Logic Device), PLA (Programmable Logic Array),RFID integrated circuits, smart chip, or any other device or arrangementof devices that is capable of implementing the steps of the processes ofthe invention.

It should be appreciated that the processors and/or memories of thecomputer system need not be physically in the same location. Each of theprocessors and each of the memories used by the computer system may bein geographically distinct locations and be connected so as tocommunicate with each other in any suitable manner. Additionally, it isappreciated that each of the processor and/or memory may be composed ofdifferent physical pieces of equipment.

A user may enter commands and information into the systems that embodythe invention through a user interface that includes input devices suchas a keyboard and pointing device, commonly referred to as a mouse,trackball or touch pad. Other input devices may include a microphone,joystick, game pad, satellite dish, scanner, voice recognition device,keyboard, touch screen, toggle switch, pushbutton, or the like. Theseand other input devices are often connected to the processing unitthrough a user input interface that is coupled to the system bus, butmay be connected by other interface and bus structures, such as aparallel port, game port or a universal serial bus (USB).

The systems that embody the invention may communicate with the user vianotifications sent over any protocol that can be transmitted over apacket-switched network or telecommunications network. By way ofexample, and not limitation, these may include SMS messages, email(SMTP) messages, instant messages (GCHAT, AIM, JABBER, etc.), socialplatform messages (Facebook posts and messages, Twitter direct messages,tweets, retweets, etc.), and mobile push notifications (iOS, ANDROID,WINDOWS, BLACKBERRY).

One or more monitors or display devices may also be connected to thesystem bus via an interface. In addition to display devices, computersmay also include other peripheral output devices, which may be connectedthrough an output peripheral interface. The computers implementing theinvention may operate in a networked environment using logicalconnections to one or more remote computers, the remote computerstypically including many or all of the elements described above.

Although internal components of the computer are not shown, those ofordinary skill in the art will appreciate that such components and theinterconnections are well known. Accordingly, additional detailsconcerning the internal construction of the computer need not bedisclosed in connection with the invention.

What is claimed is:
 1. A method for discovering social accounts acrosssocial network platforms, the method comprising: receiving, by acomputer, entity-specific input data from an entity, the entity-specificinput data comprising at least one of search strings, configurationparameters, or user credentials associated with the entity; preparing,by the computer based at least on the entity-specific input data,queries for disparate social network platforms; sending, by the computerthrough a plurality of application programming interfaces, the queriesto the disparate social network platforms; receiving, by the computer,responses to the queries from the disparate social network platforms;normalizing, by the computer, the responses from the disparate socialnetwork platforms, the normalizing including: identifying metadata inthe responses that is common across the disparate social networkplatforms; and formatting the metadata into a common format; storing, bythe computer, the metadata in the common format in a unified data store;processing, by the computer, the metadata in the common format intodiscrete categories of brand relevance or affiliation associated withthe entity; and generating, by the computer, a normalized view ofactivities across the discrete categories of brand relevance oraffiliation on the disparate social networking platforms or a portionthereof.
 2. The method according to claim 1, further comprising: addingadditional data to the responses from the disparate social networkplatforms, the additional data including categorization orclassification metadata.
 3. The method according to claim 1, wherein thesearch strings comprise at least two terms and a probabilistic weightingthat indicates a relationship strength between the at least two terms.4. The method according to claim 1, further comprising: providing a userinterface adapted for naming and saving the responses as a distinctdiscovery search.
 5. The method according to claim 4, wherein the userinterface is further adapted for setting to run the distinct discoverysearch automatically or periodically.
 6. The method according to claim1, further comprising: filtering the responses from the disparate socialnetwork platforms based at least one of: a date, new content since lastquery, or a predefined criterion.
 7. The method according to claim 1,further comprising: tracking the discrete categories individually or inaggregate in relation with the entity.
 8. A system for discoveringsocial accounts across social network platforms, the system comprising:a processor; a non-transitory computer-readable medium; and storedinstructions translatable by the processor for: receivingentity-specific input data from an entity, the entity-specific inputdata comprising at least one of search strings, configurationparameters, or user credentials associated with the entity; preparing,based at least on the entity-specific input data, queries for disparatesocial network platforms; sending, through a plurality of applicationprogramming interfaces, the queries to the disparate social networkplatforms; receiving responses to the queries from the disparate socialnetwork platforms; normalizing the responses from the disparate socialnetwork platforms, the normalizing including: identifying metadata inthe responses that is common across the disparate social networkplatforms; and formatting the metadata into a common format; storing themetadata in the common format in a unified data store; processing themetadata in the common format into discrete categories of brandrelevance or affiliation associated with the entity; and generating anormalized view of activities across the discrete categories of brandrelevance or affiliation on the disparate social networking platforms ora portion thereof.
 9. The system of claim 8, wherein the storedinstructions are further translatable by the processor for: addingadditional data to the responses from the disparate social networkplatforms, the additional data including categorization orclassification metadata.
 10. The system of claim 8, wherein the searchstrings comprise at least two terms and a probabilistic weighting thatindicates a relationship strength between the at least two terms. 11.The system of claim 8, wherein the stored instructions are furthertranslatable by the processor for: providing a user interface adaptedfor naming and saving the responses as a distinct discovery search. 12.The system of claim 11, wherein the user interface is further adaptedfor setting to run the distinct discovery search automatically orperiodically.
 13. The system of claim 8, wherein the stored instructionsare further translatable by the processor for: filtering the responsesfrom the disparate social network platforms based at least one of: adate, new content since last query, or a predefined criterion.
 14. Thesystem of claim 8, wherein the stored instructions are furthertranslatable by the processor for: tracking the discrete categoriesindividually or in aggregate in relation with the entity.
 15. A computerprogram product for discovering social accounts across social networkplatforms, the computer program product comprising a non-transitorycomputer-readable medium storing instructions translatable by aprocessor for: receiving entity-specific input data from an entity, theentity-specific input data comprising at least one of search strings,configuration parameters, or user credentials associated with theentity; preparing, based at least on the entity-specific input data,queries for disparate social network platforms; sending, through aplurality of application programming interfaces, the queries to thedisparate social network platforms; receiving responses to the queriesfrom the disparate social network platforms; normalizing the responsesfrom the disparate social network platforms, the normalizing including:identifying metadata in the responses that is common across thedisparate social network platforms; and formatting the metadata into acommon format; storing the metadata in the common format in a unifieddata store; processing the metadata in the common format into discretecategories of brand relevance or affiliation associated with the entity;and generating a normalized view of activities across the discretecategories of brand relevance or affiliation on the disparate socialnetworking platforms or a portion thereof.
 16. The computer programproduct of claim 15, wherein the instructions are further translatableby the processor for: adding additional data to the responses from thedisparate social network platforms, the additional data includingcategorization or classification metadata.
 17. The computer programproduct of claim 15, wherein the search strings comprise at least twoterms and a probabilistic weighting that indicates a relationshipstrength between the at least two terms.
 18. The computer programproduct of claim 15, wherein the instructions are further translatableby the processor for: providing a user interface adapted for naming andsaving the responses as a distinct discovery search.
 19. The computerprogram product of claim 18, wherein the user interface is furtheradapted for setting to run the distinct discovery search automaticallyor periodically.
 20. The computer program product of claim 15, whereinthe instructions are further translatable by the processor for:filtering the responses from the disparate social network platformsbased at least one of: a date, new content since last query, or apredefined criterion.